1. Field of the Invention
The present invention relates to a user-based collaborative filtering recommendation method and a system and, more specifically, a user-based collaborative filtering recommendation method and a system which amend the similarity between the recommend probable user and the other users to improve the accuracy in the prediction of preference by using the relation between the user preference information entropies of the recommend probable user and the other users as a weight value.
2. Background of the Invention
As the concept of the participation, opening and sharing on line spreads gradually, the existing boundary between the information provider and consumer is being collapsed. Therefore, there is an increasing level of expectation that the information desired by a user should exist on line necessarily. However, because a tremendous amount of information is being provided without being verified, it is worried that the reliability of the information on line is lowered. Moreover, it requires a considerable amount of time to search the desired information among the verified information. To improve these problems, so-called personalization service of providing data useful for a user draws attention recently.
The recommendation system which is one of the personalization services is a system to induce the interest of the user (who will be referred to as “the recommend probable user” hereinafter) more by recommending only the information in which the user can be interested among a large amount of information. The recommendation system analyzes the relation between the history of previous selection of the recommend probable user, the attribute information like the concern, age and gender of the recommend probable user and the information required by the recommend probable user, in order to recommend the most suitable items to the recommend probable user.
The user-based collaborative filtering recommendation system among many kinds of recommendation systems is a recommendation system that can predict the preference of the recommend probable user for an item by using preference information obtained from many users. For example, as shown in Table 1 below, in a case where users 1 to 3 express preference for a part of items 1 to 4, the recommendation system predicts the preference for the items that each user does not select (for example, in case of user 1, the preference for item 2 or 3 and in case of user 3, the preference for item 4.)
TABLE 1Item 1Item 2Item 3Item 4User 112User 22234User 3235
In order to evaluate the capacity of the collaborative filtering recommendation system, i.e., in order to know how accurate the preference predicted through the collaborative filtering recommendation system is, the capacity is evaluated by obtaining the mean absolute error between the preference predicted through the collaborative filtering recommendation system and the actual preference given by the user. The smaller the mean absolute error becomes, the more excellent the capacity is evaluated to be.
FIG. 1 illustrates the user-based collaborative filtering method. The table on the left upper end shows the S. Roh's preference for three movies of Jurassic, Paycheck and Jaws. Tables that display the preferences of H. Kwon, R. Hans and M. Lee for the movies and their similarity with the recommend probable user are illustrated in the blocks displayed by the dotted line on the right side. According to the user-based collaborative filtering method illustrated in FIG. 1, it can predict the preference for the movies that are not selected by the recommend probable user, for example, Star Wars and Memento, as shown in the table on the left lower end, by referring to the preference of the other users within the block for the movies for which the recommend probable user does not express his preference and the similarity between the S. Roh, the recommend probable user, and the other users.
However, because the user-based collaborative filtering method does not include the method of amending the similarity related to the recommend probable user, the capacity of the user-based collaborative filtering method is likely to be deteriorated. That is, the preference predicted by the user-based collaborative filtering method is likely to be greatly different from the actual preference of the recommend probable user.